recode.vars

internal

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

KM Gates

gimme

Group Iterative Multiple Model Estimation

Stephanie Lane

Zachary Fisher

Cara Arizmendi

Peter Molenaar

Edgar Merkle

Michael Hallquist

Hallie Pike

Teague Henry

Kelly Duffy

Lan Luo

Adriene Beltz

Aidan Wright

Jonathan Park

recode.vars function

<dl><dt>data</dt>
<dd>The vector of variable names to be recoded</dd>
<dt>oldvalue</dt>
<dd>A vector containing the latent variable names used internally.</dd>
<dt>newvalue</dt>
<dd>A vector containing the observed variable names, either
provided by the user (as a header) or provided by R (e.g., V1, V2).</dd></dl>

Arguments

Recode variable names. — recode.vars

<dl>

<dt>data</dt>
<dd>The vector of variable names to be recoded</dd>


<dt>oldvalue</dt>
<dd>A vector containing the latent variable names used internally.</dd>


<dt>newvalue</dt>
<dd>A vector containing the observed variable names, either
provided by the user (as a header) or provided by R (e.g., V1, V2).</dd>

</dl>

Recode variable names.

recode.vars: Recode variable names.

Description

Usage

Value

Arguments